## Popular Machine Learning Methods

Supervised Learning and Unsupervised Learning are two of the most widely used machine learning methods. However, there are other methods as well and in this article we are going to discuss about some other popular machine learning methods. Supervised Learning: As the name suggests, there is some sort of supervision involved in this set of machine learning algorithms. Here, the algorithm is trained using labeled data. For example, consider a […]

## Naive Bayes Classification

In this article we will go one step further and discuss regarding Naïve Bayes Classification. This is the second article in our machine learning for beginners’ series. In the last article, we discussed regarding the general idea of Machine Learning. Naïve Bayes Classification Algorithm is a supervised machine learning technique that is used to solve classification problems. Bayes Theorem (posterior probability) forms the core part of the Naïve Bayes algorithm. […]

## Cronbach’s Alpha

Cronbach’s alpha measures the internal consistency of a set of items. In other words, it measures how closeness of a set of items within a group. It is considered as a measure of scale reliability. If you want to test the unidimensionality of a measure, a ‘high’ value of cronbach’s alpha is not sufficient. In order to provide strong evidence that the scale or measure that we are testing is […]

## What is Stationarity in Time Series Analysis?

Stationarity in Time Series is one of the common assumptions in many of the time series analysis techniques. A stationary time series (or the underlying process) has mean, variance, and autocorrelation structure that do no change over time. Visually, a stationary time series will be a flat looking series, without trend, constant variance over time, a constant autocorrelation structure over time and no periodic fluctuations. In real-life scenarios lot of […]

## Factor Analysis

Factor Analysis is a data reduction technique that is used to reduce a data-set with large number of variables into fewer number of factors. In factor analysis technique the common variance from all the variables is extracted and it is represented as a common factor score. This score is a reduced representation (dimensional) of all the variables and can be used for any further analysis. As a part of the […]